Building a Culture of Equity, Diversity, Inclusion and Social Justice at a Community College
Bibliographic record
Abstract
Over the past number of years, it has become apparent that Canada is facing a significant skilled labour shortage. As the world emerges from the Covid-19 pandemic, the pressure to train and hire skilled trades workers has only increased; in order to fill the growing labour gap, intentional measures are needed to attract and retain a more diverse group of students. This organizational improvement plan aims to capitalize on a recent strategic plan and its central commitment to equity, diversity, inclusion and social justice. Through a case study focused on balanced enrolment by gender in trades training, this document considers a number of potential solutions before focusing on policies and practices related to recruitment, application, and admission of women into two trades programs. Principles of transformational leadership and transformative leadership theory are entwined with a change roadmap and a change process of appreciative inquiry to create a matrix for change at College on the Water (a pseudonym). A project community map is created and key responsibilities are outlined, as are the critical elements of a communication plan. Lessons learned from this case study can be applied to other programs that are inequitable in representation, whether by gender, socioeconomic status, race, or other differentiation. The immediate goal is to ameliorate an imbalance in student enrolment, and the larger goal is to help the institution’s social justice focus and responsibilities progress. To this end, a global benchmarking tool will be used to measure the institution’s current state and to set a pathway for a better future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.035 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".